English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Background Recent advances in ChIP-seq technologies have led to the identification of thousands of TP53 binding loci in various cell types, providing unmatched opportunities for analysis and comparison of the TP53 genome-wide binding patterns under different experimental conditions. These ChIP-seq datasets provide valuable resources for studying the function of TP53. However, there are currently no databases available for easily comparing and analyzing TP53 genome-wide binding patterns derived from different cell lines. Moreover, the TP53 ChIP-seq datasets are scattered among different papers, so extensive work is required to collect and process them for further analysis. Description To solve these problems, we comprehensively collected 13 publicly available TP53 ChIP-seq datasets derived from various cell lines. We re-mapped these 13 ChIP-seq datasets to the most updated reference human genome hg38 and identified the binding peaks (regions with significant enrichment of TP53 binding) and the target genes of TP53 in the human genome using the same data processing pipeline. Note that processing these 13 ChIP-seq datasets using the same pipeline is very crucial because it makes comparing the identified peaks and target genes of TP53 from different datasets possible. Finally, we developed a web-based platform (called the p53BLD), which provides a browse mode to visualize the binding loci of TP53 in the genome and a search mode to retrieve genes whose promoters are bound by TP53. The search mode is very powerful. Users can apply union, intersect, and/or difference operations on the 13 ChIP-seq datasets to generate a list of TP53 binding target genes that satisfies the users’ specifications. The generated gene list can then be downloaded for further analysis. Therefore, the p53BLD can also be regarded as a discovery tool that helps users to generate interesting gene lists for studying TP53.Conclusions Here we presented the first p53 Binding Loci Database (p53BLD). In the case study, we showed that using p53BLD can identify novel TP53 binding targets (KAT6A and KMT2A) in specific cancer cell lines. We believe that p53BLD is a useful resource for studying the function of TP53 in different cancer cell lines. P53BLD is available online at link1/, link2/, or link3/

p53 Binding Loci Database (p53BLD): a repository for the genome-wide binding loci of human TP53